Young-Jin Cha

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 477 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #17,131 of 21,575 by trust here, #14,822 by citations
• Artificial Intelligence — 36 citations into it
• Civil and Structural Engineering — 32 citations into it; #636 of 6,160 by trust here, #727 by citations
• Model Architectures — 22 citations into it
• Optimization & Training — 16 citations into it
• Ocean Engineering — 9 citations into it; #420 of 3,193 by trust here, #172 by citations
• Media Technology — 8 citations into it; #2,717 of 3,621 by trust here, #2,033 by citations
• Mechanical Engineering — 6 citations into it; #3,053 of 8,300 by trust here, #3,966 by citations

Whom Young-Jin Cha trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ross B. Girshick
4. Kaiming He
5. Yann LeCun
6. Alex Krizhevsky
7. Vinod Nair
8. Shaoqing Ren
9. Diederik P. Kingma
10. L. Eon Bottou

Who trusts Young-Jin Cha overall

1. Billie F. Spencer Jr.
2. Wooram Choi
3. Yuqing Gao
4. Sattar Dorafshan
5. Shengyuan Li
6. Dongho Kang
7. Vedhus Hoskere
8. Khalid M. Mosalam
9. D. Dais
10. Mohsen Azimi

Papers from Young-Jin Cha's perspective

Ordered by how strongly the researchers Young-Jin Cha trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters (2017) — 190 citations
• Vision-based detection of loosened bolts using the Hough transform and support vector machines (2016) — 308 citations
• SDDNet: Real-Time Crack Segmentation (2020) — 394 citations
• Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning (2020) — 367 citations
• Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types (2018) — 1,419 citations
• Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging (2018) — 352 citations
• Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks (2017) — 2,960 citations